Signed Distance Function Reconstruction
Signed Distance Function Reconstruction is a surface reconstruction method that represents the reconstructed geometry as the zero level set of a scalar field, where the field value at each point corresponds to the signed distance to the nearest surface, thereby distinguishing it by explicitly encoding both the spatial proximity and orientation relative to the surface.
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2K
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115
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57
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2017–2026
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DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation
Jeong Joon Park, Pete Florence, Julian Straub et al. · 2019 · 259 citations · Full text
Signing the Unsigned: Robust Surface Reconstruction from Raw Pointsets
Patrick Mullen, Fernando de Goes, Mathieu Desbrun et al. · Computer Graphics Forum · 2010 · 119 citations · Full text
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Tsinghua University
Beijing, China
4 h-index
9 publications
351 citations
Wayne State University
Detroit, United States
3 h-index
3 publications
122 citations
ETH Zurich
Zurich, Switzerland
3 h-index
9 publications
558 citations
Seoul National University
Seoul, South Korea
2 h-index
4 publications
98 citations
Technical University of Munich
Munich, Germany
2 h-index
4 publications
101 citations
Georg Simon Ohm University of Applied Sciences Nuremberg
Nuremberg, Germany
2 h-index
19 publications
359 citations
Brown University
Providence, United States
2 h-index
7 publications
546 citations
Institut national de recherche en sciences et technologies du numérique
Le Chesnay-Rocquencourt, France
2 h-index
3 publications
252 citations
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Repository · 4 publications · 138 citations
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TOG · 3 publications · 70 citations
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2 publications · 53 citations
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2 publications · 342 citations
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Repository · 1 publications · 10 citations
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